
class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[str]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			None
    __magic_name__								=			None
    __magic_name__								=			graph

    self._normalize_graph(UpperCamelCase__     ,				UpperCamelCase__       )
    __magic_name__								=			len(UpperCamelCase__       )
    __magic_name__								=			None
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Dict       )     ->      Tuple:







    """simple docstring"""






    if sources is int:
      __magic_name__								=			[sources]
    if sinks is int:
      __magic_name__								=			[sinks]

    if len(UpperCamelCase__       ) == 0 or len(UpperCamelCase__       ) == 0:
      return

    __magic_name__								=			sources[0]
    __magic_name__								=			sinks[0]

    # make fake vertex if there are more
    # than one source or sink
    if len(UpperCamelCase__       ) > 1 or len(UpperCamelCase__       ) > 1:
      __magic_name__								=			0
      for i in sources:
        max_input_flow += sum(self.graph[i]       )

      __magic_name__								=			len(self.graph       ) + 1
      for room in self.graph:
        room.insert(0     ,				0       )
      self.graph.insert(0     ,				[0] * size       )
      for i in sources:
        __magic_name__								=			max_input_flow
      __magic_name__								=			0

      __magic_name__								=			len(self.graph       ) + 1
      for room in self.graph:
        room.append(0       )
      self.graph.append([0] * size       )
      for i in sinks:
        __magic_name__								=			max_input_flow
      __magic_name__								=			size - 1
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[int]:







    """simple docstring"""






    if self.maximum_flow_algorithm is None:
      raise Exception("""You need to set maximum flow algorithm before."""       )
    if self.source_index is None or self.sink_index is None:
      return 0

    self.maximum_flow_algorithm.execute()
    return self.maximum_flow_algorithm.getMaximumFlow()





  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Any       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			algorithm(self       )







class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			flow_network
    __magic_name__								=			flow_network.verticesCount
    __magic_name__								=			flow_network.sourceIndex
    __magic_name__								=			flow_network.sinkIndex
    # it's just a reference, so you shouldn't change
    # it in your algorithms, use deep copy before doing that
    __magic_name__								=			flow_network.graph
    __magic_name__								=			False
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    if not self.executed:
      self._algorithm()
      __magic_name__								=			True





  def 				_lowercase				(					self     :					Any       )     ->      Any:







    """simple docstring"""






    pass







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[str]       )     ->      Dict:







    """simple docstring"""






    super().__init__(UpperCamelCase__       )
    # use this to save your result
    __magic_name__								=			-1





  def 				_lowercase				(					self     :					str       )     ->      Dict:







    """simple docstring"""






    if not self.executed:
      raise Exception("""You should execute algorithm before using its result!"""       )

    return self.maximum_flow







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Any     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__       )

    __magic_name__								=			[[0] * self.verticies_count for i in range(self.verticies_count       )]

    __magic_name__								=			[0] * self.verticies_count
    __magic_name__								=			[0] * self.verticies_count
  def 				_lowercase				(					self     :					List[Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.verticies_count

    # push some substance to graph
    for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]       ):
      self.preflow[self.source_index][nextvertex_index] += bandwidth
      self.preflow[nextvertex_index][self.source_index] -= bandwidth
      self.excesses[nextvertex_index] += bandwidth

    # Relabel-to-front selection rule
    __magic_name__								=			[
        i
        for i in range(self.verticies_count       )
        if i != self.source_index and i != self.sink_index
    ]

    # move through list
    __magic_name__								=			0
    while i < len(UpperCamelCase__       ):
      __magic_name__								=			vertices_list[i]
      __magic_name__								=			self.heights[vertex_index]
      self.process_vertex(UpperCamelCase__       )
      if self.heights[vertex_index] > previous_height:
        # if it was relabeled, swap elements
        # and start from 0 index
        vertices_list.insert(0     ,				vertices_list.pop(UpperCamelCase__       )       )
        __magic_name__								=			0
      else:
        i += 1

    __magic_name__								=			sum(self.preflow[self.source_index]       )
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Tuple       )     ->      Dict:







    """simple docstring"""






    while self.excesses[vertex_index] > 0:
      for neighbour_index in range(self.verticies_count       ):
        # if it's neighbour and current vertex is higher
        if (
            self.graph[vertex_index][neighbour_index]
            - self.preflow[vertex_index][neighbour_index]
            > 0
            and self.heights[vertex_index] > self.heights[neighbour_index]
        ):
          self.push(UpperCamelCase__     ,				UpperCamelCase__       )

      self.relabel(UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			min(
        self.excesses[from_index]     ,				self.graph[from_index][to_index] - self.preflow[from_index][to_index]     ,				)
    self.preflow[from_index][to_index] += preflow_delta
    self.preflow[to_index][from_index] -= preflow_delta
    self.excesses[from_index] -= preflow_delta
    self.excesses[to_index] += preflow_delta





  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			None
    for to_index in range(self.verticies_count       ):
      if (
          self.graph[vertex_index][to_index]
          - self.preflow[vertex_index][to_index]
          > 0
      ) and (min_height is None or self.heights[to_index] < min_height):
        __magic_name__								=			self.heights[to_index]

    if min_height is not None:
      __magic_name__								=			min_height + 1


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  [0]
    __lowerCAmelCase     :	Tuple       			=  [3]
    # graph = [
    #     [0, 0, 4, 6, 0, 0],
    #     [0, 0, 5, 2, 0, 0],
    #     [0, 0, 0, 0, 4, 4],
    #     [0, 0, 0, 0, 6, 6],
    #     [0, 0, 0, 0, 0, 0],
    #     [0, 0, 0, 0, 0, 0],
    # ]
    __lowerCAmelCase     :	str       			=  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

    # prepare our network
    __lowerCAmelCase     :	int       			=  FlowNetwork(graph, entrances, exits)
    # set algorithm
    flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
    # and calculate
    __lowerCAmelCase     :	Any       			=  flow_network.find_maximum_flow()

    print(F'''maximum flow is {maximum_flow}''')



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]=True       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			()

    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )
    __magic_name__								=			resnets

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					int=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			()

    for resnet in self.resnets:
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Dict=True       )     ->      Union[str, Any]:







    """simple docstring"""






    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					str=True       )     ->      Dict:







    """simple docstring"""






    for resnet in self.resnets:
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        FlaxResnetBlockaD(
            in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
    ]

    __magic_name__								=			[]

    for _ in range(self.num_layers       ):
      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.in_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.in_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int=True       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.resnets[0](UpperCamelCase__     ,				UpperCamelCase__       )
    for attn, resnet in zip(self.attentions     ,				self.resnets[1:]       ):
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    return hidden_states

